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摘要:
在智能体路径规划中，蚁群算法是较为流行的路径求解策略，且得到了广泛的应用。然而，传统蚁群算法存在局部最优和多余拐点问题。基于此，提出自适应多态蚁群优化算法，通过多群体划分和协作机制，极大的提高了搜索和收敛速度，有助于增强全局搜索能力，避免陷入局部最优解。改进的信息素更新策略和路径选择记录表构造进一步提高路径规划的准确性。通过3次B样条平滑曲线对路径进行处理，有效减少拐点，实现路径的平滑化。经过MATLAB和机器人操作系统(ROS)-Gazebo仿真验证，结果表明：所提算法在复杂环境下具有良好的可行性。综上所述，所提算法为智能体全局搜索带来了显著的优化和改进。
Abstract:In the realm of intelligent agent path planning, the ant colony algorithm stands as a prominent path-solving strategy that has garnered extensive adoption. However, conventional ant colony algorithms exhibit issues pertaining to local optima and excessive inflection points. The adaptive polymorphic ant colony optimization algorithm, through the mechanism of multi-colony partitioning and collaboration, remarkably enhances the search and convergence velocities, thereby bolstering global search capabilities and circumventing entrapment within local optima. To significantly increase the accuracy of path planning, this research proposes an enhanced pheromone updating technique and path selection record table building. Lastly, the adoption of trice B-Spline smoothing curves facilitates an effective reduction in inflection points, achieving path smoothness. Substantiated by MATLAB and robot operating system (ROS)-Gazebo simulations, the results demonstrate the algorithm's sound feasibility in complex environments. In conclusion, the global search of the intelligent agent is greatly optimized and enhanced by the suggested enhanced adaptive polymorphic ant colony method.
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Key words:
- path planning /
- adaptive polymorphic ant colony algorithm /
- B-Spline /
- robot operating system /
- Gazebo
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图 2 轨迹拐点与B-Spline平滑对比
Figure 2. Comparison of trajectory inflection points and B-Spline smoothing
图 4 传统蚁群算法蚂蚁路线与改进算法对比
Figure 4. Comparison between traditional ant colony algorithm ant route and improved algorithm
图 5 复杂特殊场景下本文算法轨迹
Figure 5. Proposed algorithm trajectories in complex special scenarios
图 6 基于改进A*的蚁群算法生成的轨迹
Figure 6. Trajectory generated by an ant colony algorithm based on improved A*
表 1 实验参数设置表
Table 1. Experimental Parameter Setting Table
参数 数值 起点 381 终点 18 蚂蚁个数$m$ 50 最大迭代次数$N$ 100 信息素因子$\alpha $ 1 启发函数因子$\beta $ 5 信息素挥发因子$\rho $ 0.6 信息素增加强度系数$Q$ 1 折扣系数$ (1 - \lambda ) $ 0.7 
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